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@ARTICLE{Quesnel:255496,
      author       = {Quesnel, Agathe and Coles, Nathan and Angione, Claudio and
                      Dey, Priyanka and Polvikoski, Tuomo M and Outeiro, Tiago F
                      and Islam, Meez and Khundakar, Ahmad A and Filippou,
                      Panagiota S},
      title        = {{G}lycosylation spectral signatures for glioma grade
                      discrimination using {R}aman spectroscopy.},
      journal      = {BMC cancer},
      volume       = {23},
      number       = {1},
      issn         = {1471-2407},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DZNE-2023-00297},
      pages        = {174},
      year         = {2023},
      note         = {CC BY},
      abstract     = {Gliomas are the most common brain tumours with the
                      high-grade glioblastoma representing the most aggressive and
                      lethal form. Currently, there is a lack of specific glioma
                      biomarkers that would aid tumour subtyping and minimally
                      invasive early diagnosis. Aberrant glycosylation is an
                      important post-translational modification in cancer and is
                      implicated in glioma progression. Raman spectroscopy (RS), a
                      vibrational spectroscopic label-free technique, has already
                      shown promise in cancer diagnostics.RS was combined with
                      machine learning to discriminate glioma grades. Raman
                      spectral signatures of glycosylation patterns were used in
                      serum samples and fixed tissue biopsy samples, as well as in
                      single cells and spheroids.Glioma grades in fixed tissue
                      patient samples and serum were discriminated with high
                      accuracy. Discrimination between higher malignant glioma
                      grades (III and IV) was achieved with high accuracy in
                      tissue, serum, and cellular models using single cells and
                      spheroids. Biomolecular changes were assigned to alterations
                      in glycosylation corroborated by analysing glycan standards
                      and other changes such as carotenoid antioxidant content.RS
                      combined with machine learning could pave the way for more
                      objective and less invasive grading of glioma patients,
                      serving as a useful tool to facilitate glioma diagnosis and
                      delineate biomolecular glioma progression changes.},
      keywords     = {Humans / Spectrum Analysis, Raman: methods / Glycosylation
                      / Glioma: pathology / Brain Neoplasms: pathology /
                      Glioblastoma: pathology / Neoplasm Grading / Biomolecular
                      signatures (Other) / Diagnosis (Other) / Glioblastoma
                      (Other) / Gliomas (Other) / Glycosylation (Other) / Raman
                      spectroscopy (Other)},
      cin          = {AG Fischer},
      ddc          = {610},
      cid          = {I:(DE-2719)1410002},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36809974},
      pmc          = {pmc:PMC9942363},
      doi          = {10.1186/s12885-023-10588-w},
      url          = {https://pub.dzne.de/record/255496},
}